where \(\Sigma _k\)  is the \(2 \times 2\)  covariance matrix predicted by the network for the object center \(u_k\) , and \(\rho _H\)  is the Huber norm which reduces the impact of outliers during the optimization. We initialize each object's translation \(t_{wo}\)  using the diagonal length of its 2D bounding box, similar to [1]}, from the first frame. With the known camera pose \(T_{wc, k}\) , we perform object association based on epipolar geometry constraints and the estimated translation \(t_{wo, 1:k-1}\)  up to viewpoint \(k-1\) . Detections that are not associated with any existing objects are initialized as new objects.
